Decoding the activity of neuronal populations in macaque primary visual cortex

Visual function depends on the accuracy of signals carried by visual cortical neurons. Combining information across neurons should improve this accuracy because single neuron activity is variable. We examined the reliability of information inferred from populations of simultaneously recorded neurons in macaque primary visual cortex. We considered a decoding framework that computes the likelihood of visual stimuli from a pattern of population activity by linearly combining neuronal responses and tested this framework for orientation estimation and discrimination. We derived a simple parametric decoder assuming neuronal independence and a more sophisticated empirical decoder that learned the structure of the measured neuronal response distributions, including their correlated variability. The empirical decoder used the structure of these response distributions to perform better than its parametric variant, indicating that their structure contains critical information for sensory decoding. These results show how neuronal responses can best be used to inform perceptual decision-making

From neuronal populations to behavior: a computational journey

Cognitive behaviors originate in the responses of neuronal populations. We have a reasonable understanding of how the activity of a single neuron can be related to a specific behavior. However, it is still unclear how more complex behaviors are inferred from the responses of neuronal populations. This is a particularly timely problem because multi-neuronal recording techniques have recently become increasingly available, simultaneously spurring advances in the analysis of neuronal population data. These developments are, however, constrained by the challenges of combining theoretical and experimental approaches because both approaches have their unique set of constraints. A solution to this problem is to design computational models that are either derived or inspired by cortical computations

Brain–machine interface for eye movements

A number of studies in tetraplegic humans and healthy nonhuman primates (NHPs) have shown that neuronal activity from reach-related cortical areas can be used to predict reach intentions using brain–machine interfaces (BMIs) and therefore assist tetraplegic patients by controlling external devices (e.g., robotic limbs and computer cursors). However, to our knowledge, there have been no studies that have applied BMIs to eye movement areas to decode intended eye movements. In this study, we recorded the activity from populations of neurons from the lateral intraparietal area (LIP), a cortical node in the NHP saccade system. Eye movement plans were predicted in real time using Bayesian inference from small ensembles of LIP neurons without the animal making an eye movement. Learning, defined as an increase in the prediction accuracy, occurred at the level of neuronal ensembles, particularly for difficult predictions. Population learning had two components: an update of the parameters of the BMI based on its history and a change in the responses of individual neurons. These results provide strong evidence that the responses of neuronal ensembles can be shaped with respect to a cost function, here the prediction accuracy of the BMI. Furthermore, eye movement plans could be decoded without the animals emitting any actual eye movements and could be used to control the position of a cursor on a computer screen. These findings show that BMIs for eye movements are promising aids for assisting paralyzed patients

Predicting oculomotor behaviour from correlated populations of posterior parietal neurons

Oculomotor function critically depends on how signals representing saccade direction and eye position are combined across neurons in the lateral intraparietal (LIP) area of the posterior parietal cortex. Here we show that populations of parietal neurons exhibit correlated variability, and that using these interneuronal correlations yields oculomotor predictions that are more accurate and also less uncertain. The structure of LIP population responses is therefore essential for reliable read-out of oculomotor behaviour

Inferring eye position from populations of lateral intraparietal neurons

Understanding how the brain computes eye position is essential to unraveling high-level visual functions such as eye movement planning, coordinate transformations and stability of spatial awareness. The lateral intraparietal area (LIP) is essential for this process. However, despite decades of research, its contribution to the eye position signal remains controversial. LIP neurons have recently been reported to inaccurately represent eye position during a saccadic eye movement, and to be too slow to support a role in high-level visual functions. We addressed this issue by predicting eye position and saccade direction from the responses of populations of LIP neurons. We found that both signals were accurately predicted before, during and after a saccade. Also, the dynamics of these signals support their contribution to visual functions. These findings provide a principled understanding of the coding of information in populations of neurons within an important node of the cortical network for visual-motor behaviors

Asthma, asthma control and risk of ischemic stroke:The HUNT study

Background: Asthma, a chronic inflammatory airway disease, shares common pathophysiological mechanisms with ischemic stroke. The aim of the study is to assess the association between asthma, levels of asthma control and ischemic stroke risk in men and women and by smoking habits. Methods: This prospective population-based cohort study utilized data on 58 712 adults from HUNT Study in Norway free from stroke. Self-reported asthma was categorized as ever asthma, non-active asthma and active asthma (i.e., being on asthma medication within 12 months of the baseline). Asthma control was defined ac-cording to the Global Initiative for Asthma questionnaire and was categorized into controlled and not controlled asthma. Stroke was ascertained by linking HUNT data with Nord-Trøndelag hospital records and the Norwegian Patient Registry. Results: During a mean follow-up of 17.3 �5.3 years, 2619 participants (4.5%) had a first stroke. Not controlled asthma was associated with a modest increased risk of stroke (adjusted HR 1.34, 95%CI 1.03–1.73). Subgroup analyses revealed that the respective association was stronger among those with history of smoking (HR 1.48, 95%CI 1.10–2.00) and males (HR 1.55, 95%CI 1.12–2.16) while absent in non-smokers (HR 1.02, 95%CI 0.61–1.70) and females (HR 1.05, 95%CI 0.69–1.60). Likewise, active asthma was associated with similar increased stroke risk among smokers and males and absent in non-smokers and females. Conclusions: Symptomatic and active asthma was associated with a modest increased relative risk for ischemic stroke in smokers and males. Future studies should clarify the difference in risks and mechanisms between different phenotypes of asthma

Thromboembolism and bleeding in systemic amyloidosis: a review

The assessment of both thromboembolic and haemorrhagic risks and their management in systemic amyloidosis have been poorly emphasized so far. This narrative review summarizes main evidence from literature with clinical perspective. The rate of thromboembolic events is as high as 5–10% amyloidosis patients, at least in patients with cardiac involvement, with deleterious impact on prognosis. The most known pro-thrombotic factors are heart failure, atrial fibrillation, and atrial myopathy. Atrial fibrillation could occur in 20% to 75% of systemic amyloidosis patients. Cardiac thrombi are frequently observed in patients, particularly in immunoglobulin light chains (AL) amyloidosis, up to 30%, and it is advised to look for them systematically before cardioversion. In AL amyloidosis, nephrotic syndrome and the use of immunomodulatory drugs also favour thrombosis. On the other hand, the bleeding risk increases because of frequent amyloid digestive involvement as well as factor X deficiency, renal failure, and increased risk of dysautonomia-related fall